スズキ　タロウ   鈴木　太郎    所属   千葉工業大学  先進工学部 未来ロボティクス学科    職種   教授
発行・発表の年月	2021/04
形態種別	学術雑誌
標題	Nlos multipath classification of gnss signal correlation output using machine learning
執筆形態	共著
掲載誌名	Sensors
掲載区分	国外
巻・号・頁	21(7)
著者・共著者	Taro Suzuki,Yoshiharu Amano
概要	This paper proposes a method for detecting non-line-of-sight (NLOS) multipath, which causes large positioning errors in a global navigation satellite system (GNSS). We use GNSS signal correlation output, which is the most primitive GNSS signal processing output, to detect NLOS multipath based on machine learning. The shape of the multi-correlator outputs is distorted due to the NLOS multipath. The features of the shape of the multi-correlator are used to discriminate the NLOS multipath. We implement two supervised learning methods, a support vector machine (SVM) and a neural network (NN), and compare their performance. In addition, we also propose an automated method of collecting training data for LOS and NLOS signals of machine learning. The evaluation of the proposed NLOS detection method in an urban environment confirmed that NN was better than SVM, and 97.7% of NLOS signals were correctly discriminated.
DOI	10.3390/s21072503
ISSN	1424-8220
PMID	33916725